The present disclosure relates generally to fabrication of integrated circuits (“ICs”) and, more particularly, to system and method for data mining and feature tracking for fab-wide prediction and control of future manufacturing processes.
Semiconductor IC wafers are produced using a plurality of processes in a wafer fabrication facility (“fab”). These processes, and associated process tools, may include, for example, one or more of thermal oxidation, diffusion, ion implantation, RTP (rapid thermal processing), CVD (chemical vapor deposition), PVD (physical vapor deposition), epitaxy, etch, and photolithography. During the fabrication stages, products (e.g., semiconductor wafers) are monitored and controlled for quality and yield using metrology tools. As IC feature sizes are reduced, the amount of monitoring and control may need to be increased. This in turn increases costs, due to the need for additional metrology tools, additional manpower for performing the monitoring and control, and associated delay in manufacturing cycle time.
Historical wafer manufacturing data provided by process and metrology tools employed in the fab is commonly used by process control systems for prediction and control of future processes in the fab. Currently, the historical manufacturing data is filtered using some set of criteria to obtain data that is “useful” for a particular purpose (e.g., as affecting a measurement of interest) and then the filtered data is input to a model, such a SPICE (Simulation Program with Integrated Circuit Emphasis) sensitivity model, which outputs prediction and control data. At the present time, the model used has a fixed sensitivity and the coefficients are not automatically updated. Additionally, underlying effect analysis for data clustering is not taken into account and the model is not able to meet complicated production circumstances.